e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns - A Case Study with Potato


Delineation of soil management zones in agricultural fields using reliable indicators is a major issue in precision agriculture. The leaf area index (LAI) is an important variable for the characterization of in-field variability. However, ground LAI measurement over large fields is time consuming. Our objective was to compare machine learning methods to describe in-field potato LAI patterns from airborne multispectral images. To this aim, intensive ground LAI measurements (97 quadrats) were collected in a potato field at the time of maximum LAI. Two methods were trained as function approximation, validated, and compared to linear regressions. The two methods were (i) multiple-layer perceptron (MLP) and (ii) least squares support vector machine (LS-SVM). After model training, spatial interpolation was performed and results were compared to a map interpolated with measured values. Both methods performed well using near-infrared and red channels as inputs. However, the gain in performance in validation over the best linear model was higher for the LS-SVM (29%) compared to the MLP (15%), and the kappa coefficient of agreement was higher during classification. The LS-SVM with 2 inputs (near-infrared and red) was therefore retained as the final model.

  • CA
  • Univ_Laval (CA)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.